Quantitative Methods for Food Systems Research

Chris Donovan

Food Systems Data Scientist
Food Systems Research Institute

November 5, 2025

Outline

  1. Quantitative Methods in Food Systems
  2. Approaching Quantitative Analyses
  3. Food Systems Data Survey

1. Quantitative Methods in Food Systems

Common Modeling Frameworks

  • Old friends
    • T-test
    • ANOVA
    • Regression
  • Broader frameworks
    • Dimension reduction
    • Factor analysis
    • Structural equation model
  • System modeling
    • Fuzzy cognitive mapping
    • Network
    • Agent-based
    • System dynamics
  • Where are they good for?
  • Where are they limited?
Description of image
Models identified from a review of studies on retail food environments (Mui et al. 2025)
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Share of publications over time in agricultural complex system modeling (Monasterolo et al. 2016)

(Mis-)Use and (Mis-)Interpretation

Graph of p values and posteriors
  • What is a p-value?
  • Example:
    • You’re studying whether payments for ecosystem services increase adoption of conservation agriculture practices
    • Farms that received payments were significantly more likely to adopt \((p = 0.04)\)
    • What are the chances that PES has a causal effect on adoption?
    • What are the chances that PES is significantly associated with adoption?
  • What it gives: chance of the data given the null hypothesis
  • What you want: chance of the null hypothesis given the data
Graph of p values and posteriors
Plot of p-values and posterior probabilities (Anderson 2020)

2. Approaching Quantitative Analyses

Approaching Quantitative Analyses

  • Whiteboard exercise
    • What other variables are relevant?
    • Directed acyclic graph (DAG)
  • Think about:
    • Power
    • Sample size
    • Outliers
  • Consider preregistration (Nosek et al. 2018)
Directed acyclic graph
Directed Acyclic Graph
  • GUI vs scripting
    • Ease of use
    • Reproducibility
    • Versatility
  • Also consider
    • Source
    • Price
    • Freedom
  • Scripting
    • R
    • Python
    • Julia
    • Many more
Chart showing most popular languages, python is at the top, R is third behind SQL
Anaconda State of Data Science 2021
  • Explore:
    • Distributions
    • Missing data
    • Outliers
  • Problems
    • HARKing
    • P-hacking
  • Hypothesis generation vs testing
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Anscombe Plots (Anscombe 1973)
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Git branching diagram
  • Resources
    • Documentation
    • E-books
    • DataCamp and friends
    • Videos
    • Just dig into something
  • Maria Sckolnick, statistical design and data specialist
  • Use AI scrupulously
  • Use each other!
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Python for Data Analysis, 3E R for Data Science (2e)

3. Food Systems Data Survey

Introduction

  • Food systems
  • Food system sustainability
  • Sustainability Metrics (Wiltshire et al. 2024)
  • Research questions:
    • What data are available to represent the food system in the Northeast US?
    • How well do secondary metrics represent the region?
    • What are the trends and characteristics of the available data?
Chart of environment dimension
Dimension, indices, and indicators for the Environment dimension (left to right)

Methods

  • Survey of sources
    • Federal, academic, non-profit, citizen science
    • County-level data if possible
    • Compile data beginning in 2000
  • Analysis
    • Coverage by dimension, geography, time
    • Correlations
    • Spatial regression
      • \(\Large y = X\beta + u\)
      • \(\Large u = \lambda Wu + \epsilon\)
Logos of primary data sources
Primary data sources

Results and Discussion

  • Increasing data availability over time
    • Big additions to NASS Census in 2017
  • Uneven dimensions
    • All Economics and Health indicators are represented
    • 60% of Social indicators have no available data
  • No distinct patterns of missingness by geography
Graph of indicator representation over time
Indicators represented by dimension over time.
  • Correlations within Health, but few others
  • Few cross-correlations between dimensions
  • Sparser correlations than theory suggests Schneider et al. (2025)
  • Implications for inference
    • Compensability and tradeoffs
Correlation matrix
Spearman correlations of county-level data using latest available time points
  • Of 32 metrics at the county level:
    • 46.9% were improving
    • 31.2% were declining
    • 31.2% stable
Logos of primary data sources
Coefficient estimates from spatial regressions of year on normalized metric values.

Conclusions

  • Use gaps as a roadmap for monitoring efforts
    • Development of metrics of social sustainability
    • Collect fine resolution data
  • Value of FAIR data (Wilkinson et al. 2016)
    • Frameworks for funding, distrbution of benefits
    • Well-documented protocols for access, transfer
    • Backward compatibility
  • Paramaterize food systems models with meaningful data
    • Who counts as a farmer? (Secchi 2025)
    • Data should the represent system
    • Appropriate scales of analysis
Social dimension graph
Dimensions, indices, indicators, and metrics for the Social dimension (left to right)

Thanks!

  • Explore the data and analyses:
  • Reach out:
  • Come to the next Food and Ideas Gathering!
    • Hosted by Sarra Talib, Food Systems PhD Candidate
    • Panel on food waste with local businesses
    • November 14th, Leahy 102, 3:00pm
FSRI and USDA logos

References

Anderson, Samantha F. 2020. “Misinterpreting p: The Discrepancy Between p Values and the Probability the Null Hypothesis Is True, the Influence of Multiple Testing, and Implications for the Replication Crisis.” Psychological Methods 25 (5): 596–609. https://doi.org/10.1037/met0000248.
Anscombe, F. J. 1973. “Graphs in Statistical Analysis.” The American Statistician, February.
Béné, Christophe, Steven D. Prager, Harold A. E. Achicanoy, Patricia Alvarez Toro, Lea Lamotte, Camila Bonilla, and Brendan R. Mapes. 2019. “Global Map and Indicators of Food System Sustainability.” Scientific Data 6 (1): 279. https://doi.org/10.1038/s41597-019-0301-5.
Chaudhary, Abhishek, David Gustafson, and Alexander Mathys. 2018. “Multi-Indicator Sustainability Assessment of Global Food Systems.” Nature Communications 9 (1): 848. https://doi.org/10.1038/s41467-018-03308-7.
Crippa, M., E. Solazzo, D. Guizzardi, F. Monforti-Ferrario, F. N. Tubiello, and A. Leip. 2021. “Food Systems Are Responsible for a Third of Global Anthropogenic GHG Emissions.” Nature Food 2 (3): 198–209. https://doi.org/10.1038/s43016-021-00225-9.
Cumming, Geoff, and Sue Finch. 2005. “Inference by Eye: Confidence Intervals and How to Read Pictures of Data.” American Psychologist 60 (2): 170–80. https://doi.org/10.1037/0003-066X.60.2.170.
Fanzo, Jessica, Lawrence Haddad, Kate R. Schneider, Christophe Béné, Namukolo M. Covic, Alejandro Guarin, Anna W. Herforth, et al. 2021. “Viewpoint: Rigorous Monitoring Is Necessary to Guide Food System Transformation in the Countdown to the 2030 Global Goals.” Food Policy 104 (October): 102163. https://doi.org/10.1016/j.foodpol.2021.102163.
Giller, Ken E., Thomas Delaune, João Vasco Silva, Katrien Descheemaeker, Gerrie van de Ven, Antonius G. T. Schut, Mark van Wijk, et al. 2021. “The Future of Farming: Who Will Produce Our Food?” Food Security 13 (5): 1073–99. https://doi.org/10.1007/s12571-021-01184-6.
Huff, Darrell. 1993. How To Lie with Statistics. WW Norton.
Lo, Leo Yu-Ho, Ayush Gupta, Kento Shigyo, Aoyu Wu, Enrico Bertini, and Huamin Qu. 2022. “Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?” Computer Graphics Forum 41 (3): 515–25. https://doi.org/10.1111/cgf.14559.
Monasterolo, Irene, Roberto Pasqualino, Anthony C. Janetos, and Aled Jones. 2016. “Sustainable and Inclusive Food Systems Through the Lenses of a Complex System Thinking Approach—A Bibliometric Review.” Agriculture 6 (3): 44. https://doi.org/10.3390/agriculture6030044.
Mui, Yeeli, Megan R. Winkler, Shanda L. Hunt, Joel Gittelsohn, and Melissa Tracy. 2025. “Simulated Retail Food Environments: A Literature Review of Systems Science Approaches to Advance Equity in Access to Healthy Diets.” Obesity Reviews 26 (5): e13887. https://doi.org/10.1111/obr.13887.
Nosek, Brian A., Charles R. Ebersole, Alexander C. DeHaven, and David T. Mellor. 2018. “The Preregistration Revolution.” Proceedings of the National Academy of Sciences 115 (11): 2600–2606. https://doi.org/10.1073/pnas.1708274114.
Ortiz-Bobea, Ariel, Toby R. Ault, Carlos M. Carrillo, Robert G. Chambers, and David B. Lobell. 2021. “Anthropogenic Climate Change Has Slowed Global Agricultural Productivity Growth.” Nature Climate Change 11 (4): 306–12. https://doi.org/10.1038/s41558-021-01000-1.
Schneider, Kate R., Jessica Fanzo, Lawrence Haddad, Mario Herrero, Jose Rosero Moncayo, Anna Herforth, Roseline Remans, et al. 2023. “The State of Food Systems Worldwide in the Countdown to 2030.” Nature Food 4 (12): 1090–110. https://doi.org/10.1038/s43016-023-00885-9.
Schneider, Kate R., Roseline Remans, Tesfaye Hailu Bekele, Destan Aytekin, Piero Conforti, Shouro Dasgupta, Fabrice DeClerck, et al. 2025. “Governance and Resilience as Entry Points for Transforming Food Systems in the Countdown to 2030.” Nature Food 6 (1): 105–16. https://doi.org/10.1038/s43016-024-01109-4.
Secchi, Silvia. 2025. “Who Is an American Farmer? Who Counts in American Agriculture?” Agriculture and Human Values, August. https://doi.org/10.1007/s10460-025-10781-6.
Trisovic, Ana, Matthew K. Lau, Thomas Pasquier, and Mercè Crosas. 2022. “A Large-Scale Study on Research Code Quality and Execution.” Scientific Data 9 (1): 60. https://doi.org/10.1038/s41597-022-01143-6.
Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1): 160018. https://doi.org/10.1038/sdata.2016.18.
Wiltshire, Serge, Brian Beckage, Chris Callahan, Lisa Chase, David Conner, Heather Darby, Jane Kolodinsky, et al. 2024. “Regional Food System Sustainability: Using Team Science to Develop an Indicator-Based Assessment Framework.” Journal of Agriculture, Food Systems, and Community Development 14 (1): 1–24. https://doi.org/10.5304/jafscd.2024.141.011.
Zhang, Sam, Patrick R. Heck, Michelle N. Meyer, Christopher F. Chabris, Daniel G. Goldstein, and Jake M. Hofman. 2023. “An Illusion of Predictability in Scientific Results: Even Experts Confuse Inferential Uncertainty and Outcome Variability.” Proceedings of the National Academy of Sciences 120 (33): e2302491120. https://doi.org/10.1073/pnas.2302491120.